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    Location-scale mixed models and goodness-of-fit assessment applied to insect ecology

    Moral, R. A. and Hinde, J. and Ortega, E. M. M. and Demétrio, C. G. B. and Godoy, W. A. C. (2020) Location-scale mixed models and goodness-of-fit assessment applied to insect ecology. Journal of Applied Statistics, 47 (10). pp. 1776-1793. ISSN 0266-4763

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    Survival models have been extensively used to analyse time-untilevent data. There is a range of extended models that incorporate different aspects, such as over dispersion/frailty, mixtures, and flexible response functions through semi-parametric models. In this work, we show how a useful tool to assess goodness-of-fit, the half-normal plot of residuals with a simulated envelope, implemented in the hnp package in R, can be used on a location-scale modelling context. We fitted a range of survival models to time-until-event data, where the event was an insect predator attacking a larva in a biological control experiment. We started with the Weibull model and then fitted the exponentiated-Weibull location-scale model with regressors both for the location and scale parameters. We performed variable selection for each model and, by producing half-normal plots with simulated envelopes for the deviance residuals of the model fits, we found that the exponentiated-Weibull fitted the data better. We then included a random effect in the exponentiated-Weibull model to accommodate correlated observations. Finally, we discuss possible implications of the results found in the case study

    Item Type: Article
    Additional Information: Cite as: Moral RA, Hinde J, Ortega EMM, Demétrio CGB, Godoy WAC. Location-scale mixed models and goodness-of-fit assessment applied to insect ecology. Journal of Applied Statistics. 2020;47(10):1776-1793. doi:10.1080/02664763.2019.1693522
    Keywords: Biological control; exponentiated models; half-normal plots with simulation envelopes; location-scale modelling; mixed survival models
    Academic Unit: Faculty of Science and Engineering > Mathematics and Statistics
    Faculty of Science and Engineering > Research Institutes > Hamilton Institute
    Item ID: 16460
    Identification Number:
    Depositing User: Rafael de Andrade Moral
    Date Deposited: 30 Aug 2022 14:59
    Journal or Publication Title: Journal of Applied Statistics
    Publisher: Ebscohost
    Refereed: Yes
    Use Licence: This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here

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